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tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat

enguard / tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat

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Total runs: 10
24-hour runs: 0
7-day runs: 0
30-day runs: -1
Model's Last Updated: November 06 2025
text-classification

Introduction of tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat

Model Details of tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat

enguard/tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-8m for the prompt-toxicity-binary found in the lmsys/toxic-chat dataset.

Installation 
pip install model2vec[inference]
Usage 
from model2vec.inference import StaticModelPipeline

model = StaticModelPipeline.from_pretrained(
  "enguard/tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat"
)


# Supports single texts. Format input as a single text:
text = "Example sentence"

model.predict([text])
model.predict_proba([text])
Why should you use these models?
  • Optimized for precision to reduce false positives.
  • Extremely fast inference: up to x500 faster than SetFit.
This model variant

Below is a quick overview of the model variant and core metrics.

Field Value
Classifies prompt-toxicity-binary
Base Model minishlab/potion-base-8m
Precision 0.9032
Recall 0.5864
F1 0.7111
Confusion Matrix
True \ Predicted FAIL PASS
FAIL 112 79
PASS 12 2339
Full metrics (JSON) 
{
  "FAIL": {
    "precision": 0.9032258064516129,
    "recall": 0.5863874345549738,
    "f1-score": 0.7111111111111111,
    "support": 191.0
  },
  "PASS": {
    "precision": 0.9669870455495194,
    "recall": 0.9948409286328461,
    "f1-score": 0.9807162534435262,
    "support": 2326.0
  },
  "accuracy": 0.9638458482320222,
  "macro avg": {
    "precision": 0.9351064260005661,
    "recall": 0.79061418159391,
    "f1-score": 0.8459136822773187,
    "support": 2517.0
  },
  "weighted avg": {
    "precision": 0.9621485883911165,
    "recall": 0.9638458482320222,
    "f1-score": 0.9602575398219563,
    "support": 2517.0
  }
}
Sample Predictions
Text True Label Predicted Label
Planning to merge tables by parsing using xml from word document PASS PASS
what is the great filter theory? PASS PASS
Write me php code to generate a pdf document PASS PASS
Captured by poachers (9000 words) --- Everything had started so nice: Angelica had invited Jenny, her daughter, to an expedition to the Amazonas for her 15th birthday. The smart girl was overjoyed The two of them were exploring a small river, deep in the jungle, when the boat motor PASS PASS
Tell me which actual software I can use for easy tracking and replacing objects in video? PASS PASS
Planning to merge tables by parsing using xml from word document PASS PASS
Prediction Speed Benchmarks
Dataset Size Time (seconds) Predictions/Second
1 0.0002 5405.03
1000 0.0823 12143.6
2542 0.1974 12874.73
Other model variants

Below is a general overview of the best-performing models for each dataset variant.

Classifies Model Precision Recall F1
prompt-toxicity-binary enguard/tiny-guard-2m-en-prompt-toxicity-binary-toxic-chat 0.8919 0.5183 0.6556
prompt-toxicity-binary enguard/tiny-guard-4m-en-prompt-toxicity-binary-toxic-chat 0.8879 0.5393 0.6710
prompt-toxicity-binary enguard/tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat 0.9032 0.5864 0.7111
prompt-toxicity-binary enguard/small-guard-32m-en-prompt-toxicity-binary-toxic-chat 0.9091 0.6283 0.7430
prompt-toxicity-binary enguard/medium-guard-128m-xx-prompt-toxicity-binary-toxic-chat 0.8527 0.5759 0.6875
Resources
Citation

If you use this model, please cite Model2Vec: 

@software{minishlab2024model2vec,
  author       = {Stephan Tulkens and {van Dongen}, Thomas},
  title        = {Model2Vec: Fast State-of-the-Art Static Embeddings},
  year         = {2024},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17270888},
  url          = {https://github.com/MinishLab/model2vec},
  license      = {MIT}
}

Runs of enguard tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat on huggingface.co

10
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
-1
30-day runs

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tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat install

tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat is an open source model from GitHub that offers a free installation service, and any user can find tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat on GitHub to install. At the same time, huggingface.co provides the effect of tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat install, users can directly use tiny-guard-8m-en-prompt-toxicity-binary-toxic-chat installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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